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However, the user defined m/ z variation window approach generated different XICs for the same data depending on the size of user defined m/ z window, Δ m/ z.
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Noted that multiple run of our approach generates different sets of rules.
However, two multifactor analytical approaches generated different results.
Respectively, these approaches generate different modes of knowledge: mode 1 knowledge production stemming from basic research that is discipline- and investigator-led, and sometimes termed 'blue skies' research; and mode 2 knowledge production emanating from applied, multidisciplinary research that is context- and problem-driven, and grounded in 'real life' [ 14].
This approach generated objectively different signals by changing the mRNA concentration in a controlled quantitative manner.
PLPT method takes a different approach on generating different signal representation by linearly precoding two adjacent MA symbols groupwisely.
Another approach to generate different signal representations, named selected sorting (SLS), has been proposed in [12, 13].
Because the random forest approach may generate different biological genes each time, we run the code 10 times.
In this paper, we propose to combine both approaches to generate different MRGs "randomly", quickly and automatically, while retaining the maximum-period property.
To assess whether different analysis approaches would generate different lists of discriminating genes, we compared the top 120 discriminators for the six ALL subgroups identified by RF in the present study to those identified by Ross et al [ 14] who used a chi-square metric.
In summary, studies that use different approaches and protocols generate different outcomes concerning microbial quantity, diversity, and composition.
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